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Frederique L Vernhes

from Encinitas, CA
Age ~60
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Also known as:
Frederique B Vernhes, Frederique Trustee, Vernhes Frederique
Phone and address:
3652 Sage Canyon Dr, Encinitas, CA 92024
(760) 942-8595
Connected to:
Laurent F Vernhes, 60Michel J Vernhes, 85David C Vernholm, 58Geneva S Verni, 43Karina L Verni, 60Lizeth Verni, 34Charles R Vernia, 35Barry V Vernick, 84Corrinne A Vernick, 49Jeffrey B Vernick, 38

Frederique Vernhes Phones & Addresses

  • 3652 Sage Canyon Dr, Encinitas, CA 92024 (760) 942-8595
  • Norwalk, CT
  • New Haven, CT
  • San Diego, CA
  • Lovettsville, VA
  • Del Mar, CA
  • 3652 Sage Canyon Dr, Encinitas, CA 92024 (760) 900-7749

Work

Company: Statwise inc Jan 2008 Position: Prinicpal statistician

Education

School / High School: Yale University 1985 to 1993

Skills

Biostatistics • Sas • Clinical Trials • Sas Programming • Data Management • Statistics • Data Mining • Cro • Statistical Modeling • Statistical Programming • R • Clinical Development • Predictive Analytics • Pharmaceutical Industry • Cdisc • Clinical Data Management • Clinical Study Design • Edc • Immunology • Graphs • Survival Analysis • Meddra • Diabetes • Logistic Regression

Languages

French • English

Emails

f***[email protected]

Industries

Pharmaceuticals

Resumes

Resumes

Frederique Vernhes Photo 1

Prinicpal Statistician

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Location:
3652 Sage Canyon Dr, Encinitas, CA 92024
Industry:
Pharmaceuticals
Work:
StatWise Inc since Jan 2008
Prinicpal Statistician
Quality Research Partners 2007 - 2008
Statistical Programmer
Orchestra Therapeutics 2005 - 2007
Senior Biostatistician
i3 Statprobe 2002 - 2005
Biostatician
HNC Software Inc Jan 1993 - Jan 2001
Sr Staff Scientist, Technical Manager
Education:
Yale University 1985 - 1993
Western Connecticut State University 1982 - 1985
Skills:
Biostatistics
Sas
Clinical Trials
Sas Programming
Data Management
Statistics
Data Mining
Cro
Statistical Modeling
Statistical Programming
R
Clinical Development
Predictive Analytics
Pharmaceutical Industry
Cdisc
Clinical Data Management
Clinical Study Design
Edc
Immunology
Graphs
Survival Analysis
Meddra
Diabetes
Logistic Regression
Languages:
French
English

Business Records

Name / Title
Company / Classification
Phones & Addresses
Frederique L. Vernhes
President
STATWISE INC
Business Services at Non-Commercial Site · Trust Management
3652 Sage Cyn Dr, Encinitas, CA 92024

Publications

Us Patents

Predictive Modeling Of Consumer Financial Behavior Using Supervised Segmentation And Nearest-Neighbor Matching

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US Patent:
7165037, Jan 16, 2007
Filed:
Dec 14, 2004
Appl. No.:
11/012812
Inventors:
Michael A. Lazarus - Del Mar CA, US
Larry S. Peranich - San Diego CA, US
Frederique Vernhes - Encinitas CA, US
A. U. Mattias Blume - San Diego CA, US
Kenneth B. Brown - San Diego CA, US
William R. Caid - San Diego CA, US
Ted E. Dunning - San Diego CA, US
Gerald R. Russell - San Diego CA, US
Kevin L. Sitze - San Diego CA, US
Assignee:
Fair Isaac Corporation - San Diego CA
International Classification:
G06Q 10/00
US Classification:
705 10
Abstract:
Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments, which are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur. Supervised segmentation is applied to merchant vectors to form merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. Consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer.

Predictive Modeling Of Consumer Financial Behavior Using Supervised Segmentation And Nearest-Neighbor Matching

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US Patent:
7533038, May 12, 2009
Filed:
Jan 15, 2007
Appl. No.:
11/623266
Inventors:
Matthias Blume - San Diego CA, US
Michael A. Lazarus - San Diego CA, US
Larry S. Peranich - San Diego CA, US
Frederique Vernhes - Encinitas CA, US
Kenneth B. Brown - San Diego CA, US
William R. Caid - San Diego CA, US
Ted E. Dunning - San Diego CA, US
Gerald R. Russell - San Diego CA, US
Kevin L. Sitze - San Diego CA, US
Assignee:
Fair Isaac Corporation - Minneapolis MN
International Classification:
G06Q 10/00
US Classification:
705 10
Abstract:
Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. The consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer.

Predictive Modeling Of Consumer Financial Behavior Using Supervised Segmentation And Nearest-Neighbor Matching

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US Patent:
RE42577, Jul 26, 2011
Filed:
Mar 22, 2010
Appl. No.:
12/729215
Inventors:
Matthias Blume - San Diego CA, US
Michael A. Lazarus - Del Mar CA, US
Larry S. Peranich - San Diego CA, US
Frederique Vernhes - Encinitas CA, US
William R. Caid - San Diego CA, US
Ted E. Dunning - San Diego CA, US
Gerald S. Russell - San Diego CA, US
Kevin L. Sitze - San Diego CA, US
Assignee:
Kuhuro Investments AG, L.L.C. - Dover DE
International Classification:
G06Q 10/00
US Classification:
705 731
Abstract:
Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. The consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer.

Predictive Modeling Of Consumer Financial Behavior Using Supervised Segmentation And Nearest-Neighbor Matching

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US Patent:
RE42663, Aug 30, 2011
Filed:
Mar 22, 2010
Appl. No.:
12/729218
Inventors:
Michael Lazarus - Del Mar CA, US
Larry S. Peranich - San Diego CA, US
Frederique Vernhes - Encinitas CA, US
A. U. Matthias Blume - San Diego CA, US
William R. Caid - San Diego CA, US
Ted E. Dunning - San Diego CA, US
Gerald R. Russell - San Diego CA, US
Kevin Sitze - San Diego CA, US
Assignee:
Kuhuro Investments AG, L.L.C. - Dover DE
International Classification:
G06Q 10/00
US Classification:
705 10
Abstract:
Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments, which are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur. Supervised segmentation is applied to merchant vectors to form merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. Consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer.

Predictive Modeling Of Consumer Financial Behavior Using Supervised Segmentation And Nearest-Neighbor Matching

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US Patent:
6839682, Jan 4, 2005
Filed:
Oct 3, 2000
Appl. No.:
09/679022
Inventors:
Matthias Blume - San Diego CA, US
Michael A. Lazarus - Del Mar CA, US
Larry S. Peranich - San Diego CA, US
Frederique Vernhes - Encinitas CA, US
William R. Caid - San Diego CA, US
Ted E. Dunning - San Diego CA, US
Gerald R. Russell - San Diego CA, US
Kevin L. Sitze - San Diego CA, US
Assignee:
Fair Isaac Corporation - San Diego CA
International Classification:
G06F 1760
US Classification:
705 10, 706 6, 705 14, 705 26
Abstract:
Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Consumer vectors are developed within the vector space, to represent interests of particular consumers by virtue of relative vector positions of consumer and merchant vectors. Various techniques, including clustering, supervised segmentation, and nearest-neighbor analysis, are applied separately or in combination to generate improved predictions of consumer behavior.
Control profile
Frederique L Vernhes from Encinitas, CA, age ~60 Get Report